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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# vim: set filetype=python:"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Latent Dirichlet Allocation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Latent Disirchlet Allocation is probabilistic model widely used for topic modelling. \n",
      "Unfortuantely posterior distribution fot this model is not analytically tractable, so the only way is to use approximation techniques. Variational Inference and collapsed Gibbs Sampling are two main approaches used for LDA, while VI is extremely fast (especially stochastic VI) it makes simplifying assumptions about structure of posterior and achives worse results than collapsed Gibbs Sampler (see pereplexity comparison in Griffiths and Steyer (2004) for details).\n",
      "LDA implementation in scikit-learn uses VI, while our implementation in skbayes uses collapsed Gibbs Sampler.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example: 20 newsgroups"
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "1. Load and Preprocess data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from time import time\n",
      "from skbayes.decomposition_models import GibbsLDA\n",
      "from sklearn.feature_extraction.text import CountVectorizer\n",
      "from sklearn.datasets import fetch_20newsgroups\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# example taken from  scikit-learn website\n",
      "n_samples = 200\n",
      "n_features = 1000\n",
      "n_topics = 10\n",
      "n_top_words = 20\n",
      "\n",
      "# load dataset\n",
      "print(\"Loading dataset...\")\n",
      "t0 = time()\n",
      "dataset = fetch_20newsgroups(shuffle=True, random_state=1,\n",
      "                             remove=('headers', 'footers', 'quotes'))\n",
      "data_samples = dataset.data\n",
      "print(\"done in %0.3fs.\" % (time() - t0))\n",
      "\n",
      "\n",
      "# Use tf (raw term count) features for LDA.\n",
      "print(\"Extracting tf features for LDA...\")\n",
      "tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=n_features,\n",
      "                                stop_words='english')\n",
      "t0 = time()\n",
      "tf = tf_vectorizer.fit_transform(data_samples)\n",
      "print(\"done in %0.3fs.\" % (time() - t0))\n",
      "print((\"Number of documents is {0}, vocabulary size is {1}\").format(tf.shape[0],tf.shape[1]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Loading dataset...\n",
        "done in 1.914s."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Extracting tf features for LDA...\n",
        "done in 2.342s."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Number of documents is 11314, vocabulary size is 1000\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "2. Run Sampler"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Use fit_transform (transform method can be used after fit, but it is very inefficient). If you want monitor convergence and progress\n",
      "set verbose = True, compute_score = True."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lda = GibbsLDA(n_topics = n_topics, n_burnin = 40, verbose = True,\n",
      "               compute_score = True)\n",
      "document_topic = lda.fit_transform(tf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "collected 0 sample in burn-in stage, time spent on sample = 9.39612221718, log-like = -4686480.07041\n",
        "collected 1 sample in burn-in stage, time spent on sample = 10.6653549671, log-like = -4626756.03565"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 2 sample in burn-in stage, time spent on sample = 9.44429898262, log-like = -4399809.67065"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 3 sample in burn-in stage, time spent on sample = 9.58955287933, log-like = -4133697.45206"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 4 sample in burn-in stage, time spent on sample = 9.60349392891, log-like = -4010599.19835"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 5 sample in burn-in stage, time spent on sample = 9.35082387924, log-like = -3888440.03899"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 6 sample in burn-in stage, time spent on sample = 9.33255815506, log-like = -3774297.72283"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 7 sample in burn-in stage, time spent on sample = 9.28641104698, log-like = -3683975.56228"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 8 sample in burn-in stage, time spent on sample = 9.33774113655, log-like = -3618021.91302"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 9 sample in burn-in stage, time spent on sample = 9.3835310936, log-like = -3572051.59172"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 10 sample in burn-in stage, time spent on sample = 9.39315605164, log-like = -3541659.77676"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 11 sample in burn-in stage, time spent on sample = 9.33668088913, log-like = -3523120.89303"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 12 sample in burn-in stage, time spent on sample = 9.34140396118, log-like = -3509283.69204"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 13 sample in burn-in stage, time spent on sample = 9.35956788063, log-like = -3500037.17455"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 14 sample in burn-in stage, time spent on sample = 9.30683112144, log-like = -3492484.39673"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 15 sample in burn-in stage, time spent on sample = 9.33208012581, log-like = -3486627.71813"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 16 sample in burn-in stage, time spent on sample = 9.33627796173, log-like = -3481341.70947"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 17 sample in burn-in stage, time spent on sample = 9.32463216782, log-like = -3477913.79938"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 18 sample in burn-in stage, time spent on sample = 9.29853391647, log-like = -3475064.35241"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 19 sample in burn-in stage, time spent on sample = 9.34120583534, log-like = -3471963.82473"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 20 sample in burn-in stage, time spent on sample = 9.37461495399, log-like = -3469891.8491"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 21 sample in burn-in stage, time spent on sample = 9.313560009, log-like = -3468791.94708"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 22 sample in burn-in stage, time spent on sample = 9.37225008011, log-like = -3467237.92383"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 23 sample in burn-in stage, time spent on sample = 9.38611698151, log-like = -3465882.48168"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 24 sample in burn-in stage, time spent on sample = 9.31665420532, log-like = -3464289.09536"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 25 sample in burn-in stage, time spent on sample = 9.30313897133, log-like = -3463780.87901"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 26 sample in burn-in stage, time spent on sample = 9.34749102592, log-like = -3462523.30778"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 27 sample in burn-in stage, time spent on sample = 9.29784083366, log-like = -3462065.05235"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 28 sample in burn-in stage, time spent on sample = 9.29486393929, log-like = -3461549.6516"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 29 sample in burn-in stage, time spent on sample = 9.35886192322, log-like = -3460334.84298"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 30 sample in burn-in stage, time spent on sample = 9.34133195877, log-like = -3458826.95356"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 31 sample in burn-in stage, time spent on sample = 9.31589889526, log-like = -3457484.86497"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 32 sample in burn-in stage, time spent on sample = 9.34170079231, log-like = -3457458.69098"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 33 sample in burn-in stage, time spent on sample = 9.37119698524, log-like = -3456326.86821"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 34 sample in burn-in stage, time spent on sample = 9.31454586983, log-like = -3455161.02493"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 35 sample in burn-in stage, time spent on sample = 9.29820895195, log-like = -3454799.5186"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 36 sample in burn-in stage, time spent on sample = 9.32142305374, log-like = -3454805.34406"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 37 sample in burn-in stage, time spent on sample = 9.29771494865, log-like = -3453190.54013"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 38 sample in burn-in stage, time spent on sample = 9.30461096764, log-like = -3453620.00428"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "collected 39 sample in burn-in stage, time spent on sample = 9.31666016579, log-like = -3451524.30536"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "3. Do some analysis on obtained results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# find top words for each topic\n",
      "def print_top_words(model, feature_names, n_top_words):\n",
      "       for topic_idx, topic in enumerate(model.word_topic_.T):\n",
      "           print(\"Topic #%d:\" % topic_idx)\n",
      "           print(\" \".join([feature_names[i]\n",
      "                       for i in topic.argsort()[:-n_top_words - 1:-1]]))\n",
      "tf_feature_names = tf_vectorizer.get_feature_names()            \n",
      "print_top_words(lda,tf_feature_names,20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Topic #0:\n",
        "god does jesus believe people say think true bible life christian just way know church question faith don point christians\n",
        "Topic #1:\n",
        "edu file com available program use information mail ftp files window image send data version code email server list pub\n",
        "Topic #2:\n",
        "ax max g9v b8f a86 pl 145 1d9 34u 0t 1t bhj 75u 3t giz 2di wm 2tm 7ey 0d\n",
        "Topic #3:\n",
        "people armenian new israel years armenians war said turkish world states jews university state children 000 research national israeli center\n",
        "Topic #4:\n",
        "drive windows thanks card dos use problem scsi disk does bit pc mac hard using memory like know software video\n",
        "Topic #5:\n",
        "key mr president chip government encryption use know public keys new clipper security think number law don used privacy stephanopoulos\n",
        "Topic #6:\n",
        "people don right like make think just gun want does case know state way sure question good doesn say really\n",
        "Topic #7:\n",
        "just don know like ve think got didn time said going did ll good really say people way right want\n",
        "Topic #8:\n",
        "space good car db use time used new like power high cost better low small work current probably ground launch\n",
        "Topic #9:\n",
        "10 00 25 15 20 12 team 11 14 16 game 17 30 13 50 games 24 18 new play\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# find number of words assigned to each topic\n",
      "plt.plot(lda.topics_,'r-')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Mk77Ybfk2YIiZdQO6Seofy4cAa2P5GGB0495S9jR0g+hs8JjSl4tx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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1070ea790>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot joint log-likelihood, it seems that Gibbs Sampler converged after\n",
      "# 15-20 iterations (however it is advised to sample for longer time\n",
      "# since what seems like convergence can be just local optimum)\n",
      "plt.plot(lda.scores_)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1090fbd90>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# topic distribution of each document can be used to measure\n",
      "# document similarity and cluster documents in groups\n",
      "import seaborn as sns\n",
      "sns.clustermap(document_topic[0:1000,:],row_cluster = True, col_cluster = False,\n",
      "               method = 'complete')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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U4lXQyc3sMK+DAEREC8y67Ls8vF5eOR6fn77I61Qzt6SK24lbIiKSDFTetGkT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\nj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       "text": [
        "<matplotlib.figure.Figure at 0x10c2fb850>"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}